def test_get_correct_pairwise_similarity_based_on_iou(self): corners1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]]) corners2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0], [0.0, 0.0, 20.0, 20.0]]) exp_output = [[2.0 / 16.0, 0, 6.0 / 400.0], [1.0 / 16.0, 0.0, 5.0 / 400.0]] boxes1 = box_list.BoxList(corners1) boxes2 = box_list.BoxList(corners2) iou_similarity_calculator = region_similarity_calculator.IouSimilarity() iou_similarity = iou_similarity_calculator.compare(boxes1, boxes2) with self.test_session() as sess: iou_output = sess.run(iou_similarity) self.assertAllClose(iou_output, exp_output)
def graph_fn(anchor_means, groundtruth_box_corners): similarity_calc = region_similarity_calculator.IouSimilarity() matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5, unmatched_threshold=0.3) box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1) target_assigner = targetassigner.TargetAssigner( similarity_calc, matcher, box_coder) anchors_boxlist = box_list.BoxList(anchor_means) groundtruth_boxlist = box_list.BoxList(groundtruth_box_corners) result = target_assigner.assign( anchors_boxlist, groundtruth_boxlist, unmatched_class_label=None) (cls_targets, cls_weights, reg_targets, reg_weights, _) = result return (cls_targets, cls_weights, reg_targets, reg_weights)
def graph_fn(anchor_means, groundtruth_box_corners, groundtruth_keypoints): similarity_calc = region_similarity_calculator.IouSimilarity() matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5, unmatched_threshold=0.5) box_coder = keypoint_box_coder.KeypointBoxCoder( num_keypoints=6, scale_factors=[10.0, 10.0, 5.0, 5.0]) target_assigner = targetassigner.TargetAssigner( similarity_calc, matcher, box_coder) anchors_boxlist = box_list.BoxList(anchor_means) groundtruth_boxlist = box_list.BoxList(groundtruth_box_corners) groundtruth_boxlist.add_field(fields.BoxListFields.keypoints, groundtruth_keypoints) result = target_assigner.assign( anchors_boxlist, groundtruth_boxlist, unmatched_class_label=None) (cls_targets, cls_weights, reg_targets, reg_weights, _) = result return (cls_targets, cls_weights, reg_targets, reg_weights)
def graph_fn(anchor_means, groundtruth_box_corners, groundtruth_labels, groundtruth_weights): similarity_calc = region_similarity_calculator.IouSimilarity() matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5, unmatched_threshold=0.5) box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1) unmatched_class_label = tf.constant([1, 0, 0, 0, 0, 0, 0], tf.float32) target_assigner = targetassigner.TargetAssigner( similarity_calc, matcher, box_coder) anchors_boxlist = box_list.BoxList(anchor_means) groundtruth_boxlist = box_list.BoxList(groundtruth_box_corners) result = target_assigner.assign( anchors_boxlist, groundtruth_boxlist, groundtruth_labels, unmatched_class_label=unmatched_class_label, groundtruth_weights=groundtruth_weights) (_, cls_weights, _, reg_weights, _) = result return (cls_weights, reg_weights)
def build(region_similarity_calculator_config): """Builds region similarity calculator based on the configuration. Builds one of [IouSimilarity, IoaSimilarity, NegSqDistSimilarity] objects. See core/region_similarity_calculator.proto for details. Args: region_similarity_calculator_config: RegionSimilarityCalculator configuration proto. Returns: region_similarity_calculator: RegionSimilarityCalculator object. Raises: ValueError: On unknown region similarity calculator. """ if not isinstance( region_similarity_calculator_config, region_similarity_calculator_pb2.RegionSimilarityCalculator): raise ValueError( 'region_similarity_calculator_config not of type ' 'region_similarity_calculator_pb2.RegionsSimilarityCalculator') similarity_calculator = region_similarity_calculator_config.WhichOneof( 'region_similarity') if similarity_calculator == 'iou_similarity': return region_similarity_calculator.IouSimilarity() if similarity_calculator == 'ioa_similarity': return region_similarity_calculator.IoaSimilarity() if similarity_calculator == 'neg_sq_dist_similarity': return region_similarity_calculator.NegSqDistSimilarity() if similarity_calculator == 'thresholded_iou_similarity': return region_similarity_calculator.ThresholdedIouSimilarity( region_similarity_calculator_config.thresholded_iou_similarity. threshold) raise ValueError('Unknown region similarity calculator.')
def _get_target_assigner(self): similarity_calc = region_similarity_calculator.IouSimilarity() matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5, unmatched_threshold=0.5) box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1) return targetassigner.TargetAssigner(similarity_calc, matcher, box_coder)
def create_target_assigner(reference, stage=None, negative_class_weight=1.0, use_matmul_gather=False): """Factory function for creating standard target assigners. Args: reference: string referencing the type of TargetAssigner. stage: string denoting stage: {proposal, detection}. negative_class_weight: classification weight to be associated to negative anchors (default: 1.0) use_matmul_gather: whether to use matrix multiplication based gather which are better suited for TPUs. Returns: TargetAssigner: desired target assigner. Raises: ValueError: if combination reference+stage is invalid. """ if reference == 'Multibox' and stage == 'proposal': similarity_calc = sim_calc.NegSqDistSimilarity() matcher = bipartite_matcher.GreedyBipartiteMatcher() box_coder = mean_stddev_box_coder.MeanStddevBoxCoder() elif reference == 'FasterRCNN' and stage == 'proposal': similarity_calc = sim_calc.IouSimilarity() matcher = argmax_matcher.ArgMaxMatcher( matched_threshold=0.7, unmatched_threshold=0.3, force_match_for_each_row=True, use_matmul_gather=use_matmul_gather) box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder( scale_factors=[10.0, 10.0, 5.0, 5.0]) elif reference == 'FasterRCNN' and stage == 'detection': similarity_calc = sim_calc.IouSimilarity() # Uses all proposals with IOU < 0.5 as candidate negatives. matcher = argmax_matcher.ArgMaxMatcher( matched_threshold=0.5, negatives_lower_than_unmatched=True, use_matmul_gather=use_matmul_gather) box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder( scale_factors=[10.0, 10.0, 5.0, 5.0]) elif reference == 'FastRCNN': similarity_calc = sim_calc.IouSimilarity() matcher = argmax_matcher.ArgMaxMatcher( matched_threshold=0.5, unmatched_threshold=0.1, force_match_for_each_row=False, negatives_lower_than_unmatched=False, use_matmul_gather=use_matmul_gather) box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder() else: raise ValueError('No valid combination of reference and stage.') return TargetAssigner(similarity_calc, matcher, box_coder, negative_class_weight=negative_class_weight)
def _create_model(self, apply_hard_mining=True, normalize_loc_loss_by_codesize=False, add_background_class=True, random_example_sampling=False, use_keras=False): is_training = False num_classes = 1 mock_anchor_generator = MockAnchorGenerator2x2() if use_keras: mock_box_predictor = test_utils.MockKerasBoxPredictor( is_training, num_classes) else: mock_box_predictor = test_utils.MockBoxPredictor( is_training, num_classes) mock_box_coder = test_utils.MockBoxCoder() if use_keras: fake_feature_extractor = FakeSSDKerasFeatureExtractor() else: fake_feature_extractor = FakeSSDFeatureExtractor() mock_matcher = test_utils.MockMatcher() region_similarity_calculator = sim_calc.IouSimilarity() encode_background_as_zeros = False def image_resizer_fn(image): return [tf.identity(image), tf.shape(image)] classification_loss = losses.WeightedSigmoidClassificationLoss() localization_loss = losses.WeightedSmoothL1LocalizationLoss() non_max_suppression_fn = functools.partial( post_processing.batch_multiclass_non_max_suppression, score_thresh=-20.0, iou_thresh=1.0, max_size_per_class=5, max_total_size=5) classification_loss_weight = 1.0 localization_loss_weight = 1.0 negative_class_weight = 1.0 normalize_loss_by_num_matches = False hard_example_miner = None if apply_hard_mining: # This hard example miner is expected to be a no-op. hard_example_miner = losses.HardExampleMiner( num_hard_examples=None, iou_threshold=1.0) random_example_sampler = None if random_example_sampling: random_example_sampler = sampler.BalancedPositiveNegativeSampler( positive_fraction=0.5) code_size = 4 model = ssd_meta_arch.SSDMetaArch( is_training, mock_anchor_generator, mock_box_predictor, mock_box_coder, fake_feature_extractor, mock_matcher, region_similarity_calculator, encode_background_as_zeros, negative_class_weight, image_resizer_fn, non_max_suppression_fn, tf.identity, classification_loss, localization_loss, classification_loss_weight, localization_loss_weight, normalize_loss_by_num_matches, hard_example_miner, add_summaries=False, normalize_loc_loss_by_codesize=normalize_loc_loss_by_codesize, freeze_batchnorm=False, inplace_batchnorm_update=False, add_background_class=add_background_class, random_example_sampler=random_example_sampler) return model, num_classes, mock_anchor_generator.num_anchors( ), code_size